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Spatiotemporal Evidence Accumulation Through Saccadic Sampling for Object Recognition.

Zhihao Zheng1, Jiaqi Hu1,2, Gouki Okazawa3,2

  • 1Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, International Center for Primate Brain Research, Chinese Academy of Sciences, Shanghai 200031, China.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|September 16, 2025
PubMed
Summary
This summary is machine-generated.

Object recognition during natural viewing, involving eye movements (saccades), is explained by a simple evidence accumulation model. This parsimonious approach bypasses complex visual-oculomotor interactions for understanding trans-saccadic integration.

Keywords:
decision makingevidence accumulationface recognitionobject recognitionsaccadic eye movement

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Area of Science:

  • Cognitive Neuroscience
  • Computational Vision
  • Oculomotor Systems

Background:

  • Natural object recognition relies on scanning features via eye movements (saccades).
  • Mechanistic models for saccade-based recognition integrating visual and oculomotor systems are lacking.
  • Previous studies often required controlled eye movements, limiting natural viewing insights.

Purpose of the Study:

  • To develop and test a quantitative model for object categorization during free viewing with saccades.
  • To investigate the role of sensory evidence accumulation versus oculomotor signals in trans-saccadic integration.
  • To propose a parsimonious explanation for object recognition during natural gaze behavior.

Main Methods:

  • Utilized a perceptual decision-making framework based on evidence accumulation.
  • Recruited human participants (both sexes) for face and object categorization tasks.
  • Allowed participants to freely make saccades to scan object features during categorization.

Main Results:

  • A simple evidence accumulation model quantitatively explained human categorization behavior during free viewing with saccades.
  • The model successfully fit data from naturalistic viewing conditions, unlike prior models.
  • Experimental results indicated that efference copy (saccade commands) did not significantly contribute to evidence accumulation.

Conclusions:

  • Object recognition with saccades can be effectively modeled by simple sensory evidence accumulation.
  • Complex interactions between visual and oculomotor systems are not necessary to explain saccade-based recognition.
  • This parsimonious model advances our understanding of naturalistic visual perception and decision-making.